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DOE OSTI2026

Measurement of the azimuthal anisotropy of charged particles in sNN=5.36TeV O16+O16 and Ne20+Ne20 collisions with the ATLAS detector

This paper presents the first measurements of the azimuthal anisotropy coefficients , which quantify the -order Fourier modulation of charged-particle azimuthal distributions, for in and collisions recorded with the ATLAS detector at the CERN Large Hadron Collider in 2025. The coefficients are measured as a function of transverse momentum ( ), collision centrality, and event multiplicity. They are extracted using two complementary methods: two-particle correlations with a template-fit subtraction of short-range nonflow contributions, and four-particle subevent cumulants, which intrinsically suppress nonflow effects and provide sensitivity to flow fluctuations. The results show a clear hierarchy and a nonmonotonic dependence on , reaching a maximum around , consistent with trends observed in heavy-ion collisions. Detailed comparisons between the two collision systems reveal an enhanced in central collisions, consistent with theory expectations based on the predicted prolate deformation of neon nuclei, in contrast to the slightly tetrahedral structure predicted for oxygen. The four-particle cumulant results highlight strong event-by-event fluctuations and provide the greatest sensitivity to nuclear shape effects. These measurements can place new constraints on the initial geometry and the hydrodynamic response in light-ion collisions, offering valuable input for models of nuclear structure.

Aad, G↗

DOE OSTI2021

Oridonin interrupts cellular bioenergetics to suppress glioma cell growth by down‐regulating PCK2

We aim to evaluate the tumor metabolic suppressive activity of Oridonin (extract of Rabdosia rubescens ) in glioma and elucidate its potential mechanism. Effects of Oridonin on U251/U87 cells were determined by CCK8, RTCA, colony formation, flow cytometry, wound healing, and Transwell assay. Xenograft tumor model to evaluate the effect of Oridonin on glioma cells in vivo. Cellular bioenergetics were measured by Seahorse. RNA‐seq was performed to screen potential biological pathways in Oridonin treated cells. Bioinformatics analysis of PCK2 in glioma was performed based on TCGA/CGGA. Endogenous PCK2 was knocked‐down by lentivirus packaged shRNA. We found Oridonin significantly inhibited cell growth in U251/U87 in vitro and in vivo. Both oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were decreased in Oridonin‐treated U251/U87 cells. Oridonin treatment led to PCK2 down‐regulation. Additionally, PCK2 was up‐regulated in higher grade glioma and correlated with poor outcomes. Furthermore, PCK2 depletion significantly inhibited cell growth and decreased OCR/ECAR in U251/U87 which coincided with the effects of Oridonin. Therefore, we evaluated the potent anti‐tumor property of Oridonin in glioma. Importantly, we demonstrated that PCK2 might be a novel target of Oridonin on glioma by inducing energy crisis and increasing oxidative stress.

Lin, Jianhu↗

DOE OSTI2022

Product specific thermal degradation kinetics of bisphenol F epoxy in inert and oxidative atmospheres using evolved gas analysis–mass spectrometry

Knowledge of the degradation kinetics for polymer materials is important for understanding thermal stability. In this study, evolved gas analysis–mass spectrometry and pyrolysis gas-chromatography-mass spectrometry were evaluated for the potential to deliver additional insight into thermal degradation kinetics of diglycidal ether of bisphenol F (DGEBF) epoxy thermoset under inert and oxidative atmospheres. Degradation products of selected precursor ions were evaluated for their uniqueness to the specific precursor using extracted ion thermographs. Unique mass peaks, solely attributed to a single reaction pathway of a specific product, were determined from extracted ion thermographs and used to determine both activation energy (E a ) and pre-exponential factors for the specific primary reaction pathways. These primary reaction pathways for DGEBF epoxy degradation were then evaluated in the context of transition state theory (TST) and related transition state enthalpies (ΔH ‡ ) and entropies (ΔS ‡ ) of activation to further elucidate the degradation process. It was determined under pyrolysis conditions, as suggested by the E a , the formation of bisphenol F monomer was the rate-limiting step toward the formation of xanthene and phenol. In contrast, under thermo-oxidative conditions, reactions involving oxygen containing species were identified as the rate-limiting step for all observed products based on the large negative ΔS ‡ calculated from TST. This work demonstrates a powerful combination of technique and theory that can provide new insight into the degradation of polymer materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Fluorinated Rocksalt Cathode with Ultra‐high Active Li Content for Lithium‐ion Batteries

Abstract The key to increasing the energy density of lithium‐ion batteries is to incorporate high contents of extractable Li into the cathode. Unfortunately, this triggers formidable challenges including structural instability and irreversible chemistry under operation. Here, we report a new kind of ultra‐high Li compound: Li 4+ x MoO 5 F x (1≤ x ≤3) for cathode with an unprecedented level of electrochemically active Li (>3 Li + per formula), delivering a reversible capacity up to 438 mAh g −1 . Unlike other reported Li‐rich cathodes, Li 4+ x MoO 5 F x presents distinguished structure stability to immunize against irreversible behaviors. Through spectroscopic and electrochemical techniques, we find an anionic redox‐dominated charge compensation with negligible oxygen release and voltage decay. Our theoretical analysis reveals a “reductive effect” of high‐level fluorination stabilizes the anionic redox by reducing the oxygen ions in pure‐Li conditions, enabling a facile, reversible, and high Li‐portion cycling.

Pei, Yi↗

DOE OSTI2023

New constraints on sodium production in globular clusters from the Na 23 ( He 3 , d ) Mg 24 reaction

The star-to-star anticorrelation of sodium and oxygen is a defining feature of globular clusters, but, to date, the astrophysical site responsible for this unique chemical signature remains unknown. Sodium enrichment within these clusters depends sensitively on reaction rate of the sodium destroying reactions 23 Na(p, γ) and 23 Na(p,α). In this paper, we report the results of a 23 Na( 3 He,d) 24 Mg transfer reaction carried out at Triangle Universities Nuclear Laboratory using a 21 MeV 3 He beam. Astrophysically relevant states in 24 Mg between 11 < E x < 12 MeV were studied using high-resolution magnetic spectroscopy, thereby allowing the extraction of excitation energies and spectroscopic factors. Bayesian methods are combined with the distorted wave Born approximation to assign statistically meaningful uncertainties to the extracted spectroscopic factors. For the first time, these uncertainties are propagated through to the estimation of proton partial widths. Our experimental data are used to calculate the reaction rate. The impact of the new rates are investigated using asymptotic giant branch star models. Furthermore, it is found that while the astrophysical conditions still dominate the total uncertainty, intramodel variations on sodium production from the 23 Na(p, γ) and 23 Na(p,α) reaction channels are a lingering source of uncertainty.

20 ≤ A ≤ 38↗

DOE OSTI2023

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Attend and Decode: 4D fMRI Task State Decoding Using Attention Models

Source code for Brain Attend and Decode paper. Functional magnetic resonance imaging (fMRI) is a neuroimaging modality that captures the blood oxygen level in a subject's brain while the subject either rests or performs a variety of functional tasks under different conditions. Given fMRI data, the problem of inferring the task, known as task state decoding, is challenging due to the high dimensionality (hundreds of million sampling points per datum) and complex spatio-temporal blood flow patterns inherent in the data. In this work, we propose to tackle the fMRI task state decoding problem by casting it as a 4D spatiotemporal classification problem. We present a novel architecture called Brain Attend and Decode (BAnD), that uses residual convolutional neural networks for spatial feature extraction and self-attention mechanisms for temporal modeling. We achieve significant performance gain compared to previous works on a 7-task benchmark from the large-scale Human Connectome Project-Young Adult (HCP-YA) dataset. We also investigate the transferability of BAnD's extracted features on unseen HCP tasks, either by freezing the spatial feature extraction layers and retraining the temporal model, or finetuning the entire model. The pre-trained features from BAnD are useful on similar tasks while finetuning them yields competitive results on unseen tasks/conditions.

Ng, BrendaM.↗

DOE OSTI2025

Morphological Degradation of Oxygen Evolution Reaction-Electrocatalyzing Nickel Selenides at Industrially Relevant Current Densities

We investigated electrodeposited nanoparticulate nickel selenide (pre)catalysts that transform into nickel oxides/ oxyhydroxides under oxygen evolution reaction conditions in alkaline solutions. Previous studies of this transformation were conducted at lower current densities than those of industrial relevance (≥1 A cm −2 ). We used ultramicroelectrodes (UMEs) to achieve such current densities, benefiting from their small size, ensuring low absolute currents and low ohmic drop but high current densities. Morphological degradation of the catalyst material was only observed at current densities exceeding 1 A cm −2 but not for smaller ones. Using X-ray absorption, Xray photoemission spectroscopy, and X-ray diffraction, we confirmed that the degradation was accompanied by the literature-known transformation of nanoparticulate Ni 3 Se 2 (bulk)/NiSe (surface) into nickel oxyhydroxide. The transformation of the precatalyst goes along with a significant improvement in the charge transfer kinetics observed by decreasing Tafel slopes with ongoing experimental time extracted from cyclic voltammetry (CV) experiments and electrochemical impedance spectroscopy (EIS) in the high-frequency range. However, these kinetic improvements are accompanied by limitations in mass transport concluded from decreasing current responses at high overpotentials in CVs and increasing impedance in the low-frequency range of the EIS spectra after extended CV cycling. These mass transport limitations originated from morphological degradations at the UME exceeding 1 A cm −2 which we proved by applying identical location scanning electron microscopy. This has not been reported in studies that have been limited to lower current densities before. Our findings showcase how UMEs can be used to study (pre)catalysts (herein nickel selenides) under current densities of industrial relevance in the absence of ohmic drop-related ambiguities, combined with in-depth materials characterization studies, e.g., identical location microscopy and advanced spectroscopic methods. This approach enables direct evaluation and comparison of catalyst materials and thus demonstrates how to overcome long-standing limitations of electrocatalyst design and testing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Fluorinated Rocksalt Cathode with Ultra-high Active Li Content for Lithium-ion Batteries

The key to increasing the energy density of lithium-ion batteries is to incorporate high contents of extractable Li into the cathode. Unfortunately, this triggers formidable challenges including structural instability and irreversible chemistry under operation. Here, we report a new kind of ultra-high Li compound: Li 4+x MoO 5 F x (1≤x≤3) for cathode with an unprecedented level of electrochemically active Li (>3 Li + per formula), delivering a reversible capacity up to 438 mAh g –1 . Unlike other reported Li-rich cathodes, Li 4+x MoO 5 F x presents distinguished structure stability to immunize against irreversible behaviors. In this work, through spectroscopic and electrochemical techniques, we find an anionic redox-dominated charge compensation with negligible oxygen release and voltage decay. Our theoretical analysis reveals a “reductive effect” of high-level fluorination stabilizes the anionic redox by reducing the oxygen ions in pure-Li conditions, enabling a facile, reversible, and high Li-portion cycling.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Highly durable platinum group metal-free catalyst fiber cathode MEAs for proton exchange membrane fuel cells

For this work, Fe-based platinum group metal (PGM)-free catalysts were incorporated into electrospun fiber mat or powder cathode membrane-electrode-assemblies (MEAs) with a Nafion 211 membrane and a Pt/C powder anode. Fabrication and characterization tests were performed on MEAs with: (1) a conventional powder cathode with a neat Nafion binder, (2) a fiber mat cathode with a Nafion/polyethylene oxide (PEO) binder, where PEO was extracted before MEA testing, (3) a powder cathode with a blended binder of Nafion and polyvinylidene fluoride (PVDF), and (4) a series of fiber mat cathodes with different Nafion/PVDF binder weight ratios. Cathode degradation occurred, with a loss in power output, in MEAs with a neat Nafion powder cathode or with a Nafion fiber cathode. In contrast, little or no power loss was observed for powder or fiber cathodes when the binder was a blend of Nafion and PVDF. The presence of hydrophobic PVDF drove water and electrogenerated peroxide out of the cathode, away from catalyst particles, which improved cathode durability, but PVDF also decreased the binder conductivity and slowed oxygen reduction kinetics, resulting in lower power densities. A 75:25 w:w Nafion:PVDF fiber cathode MEA was the best compromise for maximizing power and minimizing catalyst degradation. For such a cathode, with a PGM-free cathode catalyst loading of 3.0 mg cm –2 , a power density of 88 mW cm –2 at 0.5 V, 80 °C, and 200 kPa abs pressure was maintained for 80 h of continuous operation.

25 ENERGY STORAGE↗

DOE OSTI2025

Ultraselective sequestration of Li + and Mg 2+ from brines via a reusable polyoxoniobate-based ion sponge

Lithium (Li) and magnesium (Mg) are designated as critical mineral materials (CMM) due to their essential roles in clean energy technologies. However, extracting high-purity Li + from brine remains a formidable challenge owing to the presence of Mg 2+ , a physicochemical similar ion that often exists in excess. Here, we introduce a polyoxoniobate-based “Mg-PONb sponge” that enables ultraselective and rapid Li + /Mg 2+ separation across an exceptionally broad range of Mg/Li ratios (0.02 to 200.63). This framework achieves >99.9% Mg 2+ removal with negligible Li + loss in under 1 min, yielding Li + /Mg 2+ selectivity values exceeding 5000. The sponge demonstrates excellent recyclability, maintaining >99% Mg 2+ rejection and Li + permeability across five regeneration cycles without structural degradation. Mechanistic investigations reveal that selective Mg 2+ capture originates from strong coordination with terminal oxygens on the PONb cluster, driving rapid formation of porous Mg-PONb frameworks. This work presents a generalizable, scalable strategy for Li + /Mg 2+ separation and offers a sustainable path toward enhanced Li and Mg recovery from complex brine sources.

Chen, Linfeng [Lawrence Berkeley National Laborato↗

DOE OSTI2026

Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element using one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. Here, in this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.

battery↗

DOE OSTI2022

Potassium carbonate decomposition modeling within rotating detonation engines for direct power extraction applications

We report that recently, there has been a significant interest in detonation-based combustion systems, such as rotating detonation engines (RDE), due to potential performance advantages in propulsion and energy applications. This includes improvements in thermodynamic efficiency, inherently high gas velocities, and the possibility of achieving an increase in total pressure (i.e. pressure gain) through the combustion process. Due to approximating a constant volume combustion process, these devices also tend to exhibit extremely high local gas temperatures relative to a comparable constant pressure combustor. These advantages overlap well with desirable performance characteristics of direct power extraction (DPE) technologies, such as a magnetohydrodynamic (MHD) generator. Typically, in DPE systems hot combustion products are seeded with an easily ionizable material such as potassium carbonate (K 2 CO 3 ) in order to boost the electrical conductivity. However, due to the short gas residence times within an RDE, it was unclear whether forming an electrically conductive combustion plasma would be feasible for integration with a downstream MHD generator. A model is presented which describes the heating, decomposition, and ionization of solid K 2 CO 3 particles and aqueous solutions of K 2 CO 3 in water, for a given initial particle diameter. This model was combined with available computational fluid dynamics (CFD) data for an oxygen-methane RDE in a one-way coupled Eulerian-Lagrangian framework to predict particle trajectories and the corresponding heating, decomposition, and ionization histories. Electrical conductivities were computed using a previously developed model, and a method was proposed to determine an equivalent average electrical conductivity. Results show that particle sizes below ~30 µm are able to fully decompose before reaching the exit of the RDE. While the one-way coupled nature of the simulations precluded rigorous evaluation of the effects of seed material on detonability, a substantial temperature reduction is expected at the detonation wave front due to heating and decomposition. A preliminary comparison is presented between the RDE and an equivalent constant pressure adiabatic combustor, showing a potential performance advantage for the RDE.

42 ENGINEERING↗

DOE OSTI2021

Mixed Cationic and Anionic Redox in Ni and Co Free Chalcogen-Based Cathode Chemistry for Li-Ion Batteries

Mixed cationic and anionic redox cathode chemistry is emerging as conventional cationic-redox of transition metal based layered oxides are reaching their theoretical capacity limit. However, these anionic redox in transition metal oxide-based cathodes attained by taking excess lithium has resulted in stability issues due to weak metal - oxygen ligand covalency. Here, we present an alternative approach of improving metal - ligand covalency by introducing less electronegative chalcogen ligand (sulfur) in the cathode structural framework where the metal dband penetration into ligand p-band thereby utilizing reversible mixed anionic and cationic redox chemistry. Through this design strategy, we report possibility of developing a new family of layered cathode materials when partially filled d orbital redox couples like Fe 2+/3+ are introduced in the Li-ion conducting phase (Li 2 SnS 3 ). Further, the electron energy loss spectroscopy (EELS) and X-ray absorption near-edge structure (XANES) analysis are used to qualitatively identify the charge contributors at the metal and ligand sites during Li + extraction. The detailed high-resolution transmission electron microscopy (HR-TEM) and high annular dark field-scanning transmission electron microscopy (HAADF-STEM) investigation reveal the multi redox induced structural modifications and its surface amorphization with nanopore formation during cycling. Findings from this study will shed light on designing Ni and Co free chalcogen cathodes and various functional materials in the chalcogen based dual anionic and cationic redox cathode avenue.

25 ENERGY STORAGE↗

DOE OSTI2022

Digitally manufactured air plasma-on-water reactor for nitrate production

The sustainable production of food to support the increasing world population is one of humanity’s most pressing challenges. Plasma activated water, produced using renewable energy, can help fulfill plants’ needs in sustainable agriculture approaches. The design, implementation, and characterization of a digitally manufactured air plasma-on-water reactor (POWR) for the synthesis of nitrate as green nitrogen fertilizer is presented. The interaction of air plasma-generated reactive oxygen and nitrogen species with water produces nitrate (NO 3 - ) and related species, which are the main nitrogen-containing nutrients for plants. The mild conditions of the operation of the POWR opens the possibility to use plastics, particularly through digital manufacturing strategies such as 3D-printing, for its fabrication. A pin-to-plate reactor configuration powered by high-voltage alternating power is chosen due to its simplicity and efficacy. A computational thermal-fluid model is used to evaluate the design and attain expected operational characteristics. The experimental characterization of the POWR encompassed design and operation parameters, namely electrode-water spacing, air flow rate, and voltage level. A machine learning approach is implemented to extract and quantify characteristic features of the plasma–water interaction, such plasma volume and plasma–water interface area. Experimental results revealed that the nitrate production rate varies linearly with dimensionless plasma volume. The design, fabrication, and characterization methods presented can be adapted to other POWRs and help enable on-demand nitrogen fertilizer production at low environmental and economic cost.

Physics↗

DOE OSTI2025

Measurement of the azimuthal anisotropy of charged particles in $\sqrt{s_{\mathrm{NN}}}=5.36$ TeV $^{16}$O$+^{16}$O and $^{20}$Ne$+^{20}$Ne collisions with the ATLAS detector

This paper presents the first measurements of the azimuthal anisotropy coefficients $v_{n}$, which quantify the $n^{\mathrm{th}}$-order Fourier modulation of charged-particle azimuthal distributions, for $n=2$-4 in $\sqrt{s_{\mathrm{NN}}}=5.36$ TeV $\mathrm{^{16}O}+\mathrm{^{16}O}$ and $\mathrm{^{20}Ne}+\mathrm{^{20}Ne}$ collisions recorded with the ATLAS detector at the Large Hadron Collider in 2025. The $v_{n}$ coefficients are measured as a function of transverse momentum ($p_{\mathrm{T}}$), collision centrality, and event multiplicity. They are extracted using two complementary methods: two-particle correlations with a template-fit subtraction of short-range non-flow contributions, and four-particle subevent cumulants, which intrinsically suppress non-flow effects and provide sensitivity to flow fluctuations. The results show a clear hierarchy $v_{2} > v_{3} > v_{4}$ and a non-monotonic dependence on $p_{\mathrm{T}}$, reaching a maximum around 2 GeV, consistent with trends observed in heavy-ion collisions. Detailed comparisons between the two collision systems reveal an enhanced $v_{2}$ in central $\mathrm{^{20}Ne}+\mathrm{^{20}Ne}$ collisions, consistent with theory expectations based on the predicted prolate deformation of neon nuclei, in contrast to the slightly tetrahedral structure predicted for oxygen. The four-particle cumulant results highlight strong event-by-event fluctuations and provide the greatest sensitivity to nuclear shape effects. These measurements can place new constraints on the initial geometry and the hydrodynamic response in light-ion collisions, offering valuable input for models of nuclear structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

A Simple Approach for the Synthesis of Cellulose Nanofiber Reinforced Chitosan/PVP Bio Nanocomposite Film for Packaging

The development of chitosan-based food packaging film has been increasing due to its wider availability, biodegradability, low-cost, and renewability. The purpose of this study was to develop cellulose nanofibers (CNFs) reinforced Chitosan/PVP(CHP) green bio nanocomposite film by solvent casting method with enhanced barrier and mechanical property. CNFs of dimension 9–11 nm and 100–200 nm were extracted from jute fibers by directly treating it with HNO{sub 3}–NaNO{sub 2} mixture. Bio nanocomposite films were synthesized with various concentrations of CNFs and evaluated using SEM, TGA/DTG and XRD to analyze the morphology, thermal behavior and crystallinity of bio nanocomposite films. SEM analysis revealed the uniform dispersion of CNFs in CHP matrix. Thermal stability of prepared bio nanocomposite films was improved with the loading of 5 wt% of cellulose nanofibers. ATR(FT-IR) spectra shows physical interaction between all the components of bio nanocomposite film. The bio nanocomposite films were also characterized for physical property like thickness, mechanical testing and barrier properties. The addition of CNFs significantly enhanced the mechanical property of bio nanocomposite films. Furthermore, water and oxygen transmission rate of bio nanocomposite film decreased with the increase in CNFs concentration. Overall the performed studies confirmed, the use of CNFs in polymer matrix makes it possible to produce a flexible, transparent, thermally stable bio nanocomposite film with enhanced mechanical and barrier properties for food packaging applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Predicting the Rate of Degradation Related to Oxygen Electrode Delamination in Solid Oxide-Ion Electrolyzers

One of the leading causes for the performance degradation in H 2 -producing solid oxide electrolytic cells (SOECs) is the gradual delamination of oxygen electrode (OE) from the electrolyte under a strong anodic polarization. Identification of the key factor that controls the rate of OE delamination is of paramount importance to achieve long-term stable operation of SOECs. Here we show from thousands of hours of testing data that the exchange current density (i°) of OE can be leveraged as a predictor for the rate of delamination. To obtain i°, we apply DC-biased electrochemical impedance spectroscopy on a three-electrode symmetrical cell to measure polarization resistance (R p ) of OE as a function of current density (i) and time (t). The collected R p -i-t raw data are then converted to overpotential ( η )-i-t, from which i° is extracted from the “low-field” approximation. An analytical relationship between i° and time-to-delamination (TTD) is further established from the established i°-i-t relationship. We show that under a constant anodic polarization current density i, the greater the ratio i/i°, the faster the delamination. Therefore, we conclude that the exchange current density of an OE, i°, can be used to predict the rate of OE degradation in solid oxide-ion electrolyzers.

25 ENERGY STORAGE↗